Bibliographic record
Abstract
Abstract. Seasonal streamflow forecasts are an important component of flood risk management. Hybrid forecasting methods that predict seasonal streamflow using machine learning models driven by climate model outputs are currently underexplored, yet have some important advantages over traditional approaches using hydrological models. Here we develop a hybrid subseasonal to seasonal streamflow forecasting system to predict the monthly maximum daily streamflow up to four months ahead. We train a random forest machine learning model on dynamical precipitation and temperature forecasts from a multimodel ensemble of 196 members (eight seasonal climate forecast models) from the Copernicus Climate Change Service (C3S) to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016, with up to four months lead time. We show that multi-site ML models trained on pooled catchment data together with static catchment attributes are significantly more skilful compared to single-site ML models trained on data from each catchment individually. Considering all initialization months, 60 % of stations show positive skill (CRPSS > 0) relative to climatological reference forecasts in the first month after initialization. This falls to 41 % in the second month, 38 % in the third month and 33 % in the fourth month.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.314 | 0.235 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".